
# ! pip install xhtml2pdf


import numpy as np                                  
import pandas as pd                              
import seaborn as sns                                 
import plotly.express as px                   
import plotly.graph_objs as go                       
import matplotlib.pyplot as plt                  
from matplotlib import colors as mcolors                   
from plotly.offline import iplot                       
import folium                                          
from folium.plugins import HeatMap                               
from geopy.geocoders import Nominatim                                 
from wordcloud import WordCloud                                  
import re                                      
from wordcloud import WordCloud, STOPWORDS                              
from PIL import Image                                     


Original_data = pd.read_csv(r'C:\Users\Sabae\OneDrive\Desktop\zomato.csv')     # Loading Data With The Help of Pandas.
Original_data.head()


d1 = pd.read_csv(r'C:\Users\Sabae\OneDrive\Desktop\zomato.csv')  # Coping Zomato Dataset Into One Variable for Doing Some Operations.
d1.head()


pd.isnull(d1).sum()  # TO Checking Data is There Any Null Values Or Not.

url                                0
address                            0
name                               0
online_order                       0
book_table                         0
rate                            7775
votes                              0
phone                           1208
location                          21
rest_type                        227
dish_liked                     28078
cuisines                          45
approx_cost(for two people)      346
reviews_list                       0
menu_item                          0
listed_in(type)                    0
listed_in(city)                    0
dtype: int64


d1 = d1.drop(['address','phone','listed_in(city)','dish_liked','menu_item'], axis=1)      # Droping Some Column Using Drop Function.
d1.head()    # Checking Only Top Data Using head.


d1.info()        # Checking All Data Type.

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 51717 entries, 0 to 51716
Data columns (total 12 columns):
 #   Column                       Non-Null Count  Dtype 
---  ------                       --------------  ----- 
 0   url                          51717 non-null  object
 1   name                         51717 non-null  object
 2   online_order                 51717 non-null  object
 3   book_table                   51717 non-null  object
 4   rate                         43942 non-null  object
 5   votes                        51717 non-null  int64 
 6   location                     51696 non-null  object
 7   rest_type                    51490 non-null  object
 8   cuisines                     51672 non-null  object
 9   approx_cost(for two people)  51371 non-null  object
 10  reviews_list                 51717 non-null  object
 11  listed_in(type)              51717 non-null  object
dtypes: int64(1), object(11)
memory usage: 4.7+ MB


d1.drop_duplicates(inplace= True)        # Droping Dublicate Values using drop_duplicates Function.
d1.shape

(51717, 12)


d1['rate'].unique()    # Cleaning Rate Column Using Unique Function.

array(['4.1/5', '3.8/5', '3.7/5', '3.6/5', '4.6/5', '4.0/5', '4.2/5',
       '3.9/5', '3.1/5', '3.0/5', '3.2/5', '3.3/5', '2.8/5', '4.4/5',
       '4.3/5', 'NEW', '2.9/5', '3.5/5', nan, '2.6/5', '3.8 /5', '3.4/5',
       '4.5/5', '2.5/5', '2.7/5', '4.7/5', '2.4/5', '2.2/5', '2.3/5',
       '3.4 /5', '-', '3.6 /5', '4.8/5', '3.9 /5', '4.2 /5', '4.0 /5',
       '4.1 /5', '3.7 /5', '3.1 /5', '2.9 /5', '3.3 /5', '2.8 /5',
       '3.5 /5', '2.7 /5', '2.5 /5', '3.2 /5', '2.6 /5', '4.5 /5',
       '4.3 /5', '4.4 /5', '4.9/5', '2.1/5', '2.0/5', '1.8/5', '4.6 /5',
       '4.9 /5', '3.0 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5',
       '2.1 /5', '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)


def Cleaning_rate(cl_rate):               # Creating Function to Clean Rate Column.
    if(cl_rate == 'NEW' or cl_rate == '-'):               # Removing 'NEW','-' & '/5'
        return np.nan                                         # Return Output.
    else:
        return float(str(cl_rate).split('/')[0])              # Return Output.
        
d1['rate'] = d1['rate'].apply(Cleaning_rate)             # Applying Function to get Output.
d1['rate'].head()            # Checking Only Top Data Using head.

0    4.1
1    4.1
2    3.8
3    3.7
4    3.8
Name: rate, dtype: float64


d1['rate'].fillna(d1['rate'].mean(), inplace= True)   # Filing Null Values Using Fillna Function.
d1['rate'].isnull().sum()             # TO Checking Data is There Any Null Values Or Not.

0


d1.info()       # Checking All Data Type.

<class 'pandas.core.frame.DataFrame'>
Int64Index: 51717 entries, 0 to 51716
Data columns (total 12 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   url                          51717 non-null  object 
 1   name                         51717 non-null  object 
 2   online_order                 51717 non-null  object 
 3   book_table                   51717 non-null  object 
 4   rate                         51717 non-null  float64
 5   votes                        51717 non-null  int64  
 6   location                     51696 non-null  object 
 7   rest_type                    51490 non-null  object 
 8   cuisines                     51672 non-null  object 
 9   approx_cost(for two people)  51371 non-null  object 
 10  reviews_list                 51717 non-null  object 
 11  listed_in(type)              51717 non-null  object 
dtypes: float64(1), int64(1), object(10)
memory usage: 5.1+ MB


d1.dropna(inplace= True)   # Droping Null Values Using Dropna Function.
d1.head()


pd.isnull(d1).sum()  # And Now there is no any null values in the data.

url                            0
name                           0
online_order                   0
book_table                     0
rate                           0
votes                          0
location                       0
rest_type                      0
cuisines                       0
approx_cost(for two people)    0
reviews_list                   0
listed_in(type)                0
dtype: int64


d1.rename(columns= {'name':'restaurant','approx_cost(for two people)':'cost_two_plates',
                    'listed_in(type)':'type'},inplace= True)     # Renaming the Columns Using Rename Function.
d1.head()           # Checking Only Top Data Using head.


d1['cost_two_plates'].unique()                    # Checking Unique Values Using Unique Function.

array(['800', '300', '600', '700', '550', '500', '450', '650', '400',
       '900', '200', '750', '150', '850', '100', '1,200', '350', '250',
       '950', '1,000', '1,500', '1,300', '199', '80', '1,100', '160',
       '1,600', '230', '130', '50', '190', '1,700', '1,400', '180',
       '1,350', '2,200', '2,000', '1,800', '1,900', '330', '2,500',
       '2,100', '3,000', '2,800', '3,400', '40', '1,250', '3,500',
       '4,000', '2,400', '2,600', '120', '1,450', '469', '70', '3,200',
       '60', '560', '240', '360', '6,000', '1,050', '2,300', '4,100',
       '5,000', '3,700', '1,650', '2,700', '4,500', '140'], dtype=object)


d1['cost_two_plates'] = d1['cost_two_plates'].astype(str)                # Converting Object to String.
d1['cost_two_plates'] = d1['cost_two_plates'].apply(lambda i: i.replace(',',''))     # Cleaning Column Using Lambda Function.
d1['cost_two_plates'] = d1['cost_two_plates'].astype(float)             # Converting String to Float.
d1['cost_two_plates'].unique()          # Checking Unique Values Using Unique Function.

array([ 800.,  300.,  600.,  700.,  550.,  500.,  450.,  650.,  400.,
        900.,  200.,  750.,  150.,  850.,  100., 1200.,  350.,  250.,
        950., 1000., 1500., 1300.,  199.,   80., 1100.,  160., 1600.,
        230.,  130.,   50.,  190., 1700., 1400.,  180., 1350., 2200.,
       2000., 1800., 1900.,  330., 2500., 2100., 3000., 2800., 3400.,
         40., 1250., 3500., 4000., 2400., 2600.,  120., 1450.,  469.,
         70., 3200.,   60.,  560.,  240.,  360., 6000., 1050., 2300.,
       4100., 5000., 3700., 1650., 2700., 4500.,  140.])


d1.info()                # Checking All Data Type.

<class 'pandas.core.frame.DataFrame'>
Int64Index: 51148 entries, 0 to 51716
Data columns (total 12 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   url              51148 non-null  object 
 1   restaurant       51148 non-null  object 
 2   online_order     51148 non-null  object 
 3   book_table       51148 non-null  object 
 4   rate             51148 non-null  float64
 5   votes            51148 non-null  int64  
 6   location         51148 non-null  object 
 7   rest_type        51148 non-null  object 
 8   cuisines         51148 non-null  object 
 9   cost_two_plates  51148 non-null  float64
 10  reviews_list     51148 non-null  object 
 11  type             51148 non-null  object 
dtypes: float64(2), int64(1), object(9)
memory usage: 5.1+ MB


d1.groupby('location')['restaurant'].unique()            # Checking Unique Values Using Unique Function.

location
BTM                  [Sankranthi Veg Restaurant, Hearts Unlock Cafe...
Banashankari         [Jalsa, Spice Elephant, San Churro Cafe, Addhu...
Banaswadi            [Cafe Nibras, The Sanctuary, Crunch Pizzas, 9 ...
Bannerghatta Road    [Deja Vu Resto Bar, Fattoush, Empire Restauran...
Basavanagudi         [Grand Village, Timepass Dinner, Srinathji's C...
                                           ...                        
West Bangalore       [FreshMenu, Fit Dish Fetish, Garden City Mobil...
Whitefield           [Imperio Cafe, Night Diaries, LocalHost, AB's ...
Wilson Garden        [Tree Top, Sahana's (Nati Style), Karavali Kol...
Yelahanka            [Prashanth Naati Corner, Red Chillies Curries ...
Yeshwantpur          [Chef's Bank, New Agarwal Bhavan, Fishing Boat...
Name: restaurant, Length: 93, dtype: object


Restaurant = []                 # Creating Empty List.
Location = []                       # Creating Empty List.
for key,location in d1.groupby('location'):             # Using For Loop to Fetch all Data.
    Location.append(key)                                   # Taking All Data into empty list.
    Restaurant.append(len(location['restaurant'].unique()))      # Taking All Data into empty list.


Rst_total = pd.DataFrame(zip(Location,Restaurant))                # Unziping data.
Rst_total.columns = ['Location','Restaurant']                        # Creating Columns Name Using Column Function.
Rst_total.set_index('Location',inplace=True)                             # Set data Using Set_index Function.
Rst = Rst_total.sort_values(by='Restaurant').tail(10)            # Checking Only Last Data Using tail.     
Rst          # Calling data Using Rst.


fig = go.Bar(x = Rst_total['Restaurant'].nlargest(10).index,             # Ploting Bar Graph For Better Visalization.
               y = Rst_total['Restaurant'].nlargest(10),
               marker_line_color='black',marker_line_width=2)

iplot([fig])


d1['cost_two_plates'].min()           # Using Min to Check Cheapest Price of Restaurant.

40.0


d1[d1['cost_two_plates'] == 40]['restaurant']   # Cheapest Restaurant in Bangalore.

5270     Srinidhi Sagar Food Line
12232    Srinidhi Sagar Food Line
14819    Srinidhi Sagar Food Line
17773              Srinidhi Sagar
18891    Srinidhi Sagar Food Line
27091       Srinidhi Sagar Deluxe
29785       Srinidhi Sagar Deluxe
32485              Srinidhi Sagar
Name: restaurant, dtype: object


plt.figure(figsize= (5,5))           # Using Figure to Fig the Size.
plt.xticks(fontsize=10)                            # Using Xticks to Give Font Size.
Resta_no = np.array([5,90])                              # Using Numpy as np.
plt.title('Expensive V/S Cheapest')           # Giving title to Figure.
Resta_name = ['Expensive','Cheapest']                    # Giving name.
my_explode = [0,0.3]                     # Giving Explode.
plt.pie(Resta_no,labels=Resta_name,shadow=True,autopct='%1.2f%%',explode=my_explode,
        wedgeprops={'edgecolor':'k'})                  # Ploting Pie Chart for Visualization.
plt.legend(['Expensive','Cheapest'])            # Giving Legend.
plt.show()              # Showing the pie chart.


d1['cost_two_plates'].max()           # Using Max to Check Cheapest Price of Restaurant.

6000.0


d1[d1['cost_two_plates'] == 6000]['restaurant']   # Expensive Restaurant in Bangalore.

19139    Le Cirque Signature - The Leela Palace
45618    Le Cirque Signature - The Leela Palace
Name: restaurant, dtype: object


d1['cost_two_plates'].mean()       # Using Mean to Check affordable Restaurant.

556.0437358254477


R1 = d1[d1['cost_two_plates'] <= 556].loc[:,('cost_two_plates','restaurant')].head(18)  # Affordadble Restaurant in Bangalore.
R2 = R1.sort_values('cost_two_plates')             # Sort the Values Using Sort Function.
R3 = R2.drop([10],axis=0)
R3              #  Calling Data Using R3.


fig = plt.figure(figsize=(8,6))          # Using Figure to Fig the Size.
plt.xticks(rotation = 90,fontsize=15)           # Using Xticks to Give Font Size and Rotaion.
sns.set_style(style='whitegrid')            # Affordadble Restaurant in Bangalore.
sns.barplot(data=R3,x='restaurant',y='cost_two_plates',palette='turbo',
            edgecolor='black',linewidth=2)    # Ploting Barplot.
plt.title('Affordadble Restaurant in Bangalore')   # Giving Title to Figure.

Text(0.5, 1.0, 'Affordadble Restaurant in Bangalore')


R4 = d1[(d1['cost_two_plates'] <= 670) & (d1['location'] == 'BTM') &
                  (d1['rate'] >4) & 
                  (d1['rest_type'] == 'Quick Bites')].head(50)  # Creating Condition & Checking Only Top Data Using head.   
R4['restaurant'].unique()    # Checking Unique Values Using Unique Function.

array(['Swadista Aahar', 'Litti Twist', 'The Shawarma Shop', 'Gorbandh',
       'Yum In My Tum', 'Chaatimes', "Muthashy's", 'Swad Punjab Da',
       'Hungry Lee', "Domino's Pizza", 'Roti Wala'], dtype=object)


R5 = d1.groupby(['restaurant']).sum()      # Grouping the Data Using Group By.
R6 = R5.sort_values('votes',ascending=False)   # Restaurant Wise Highest Votes.
R7 = R6[['votes']].head(10)         # Checking Only Top Data Using head.
R7   # Calling Dtaa using R7.


fig = plt.figure(figsize=(15,5))     # Using Figure to Fig the Size.
sns.set_style(style='whitegrid')            # Highest Voted Restaurant.
sns.barplot(data=R7.reset_index(),x='restaurant',y='votes',palette='gist_rainbow_r',
            edgecolor='black',linewidth=1.5)   # Ploting barplot.
plt.xticks(rotation=45,horizontalalignment='right',fontsize=13)      # Using Xticks to Give Font Size and Rotaion.
plt.title('Highest Voted Restaurant')               # Giving Title to Figure.

Text(0.5, 1.0, 'Highest Voted Restaurant')


R8 = d1[(d1['rate'] >= 4) & (d1['cost_two_plates'] <= 556)]      # Creating Condition.
R9 = R8.drop([10],axis=0)
R10 = R9[['restaurant','rate','cost_two_plates']]
R10


plt.figure(figsize= (12,5))                          # Ploting Barplot.
plt.xticks(fontsize=16)
R8 = d1['restaurant'].value_counts()[0:40:3]
sns.barplot(x=R8,y=R8.index,palette='Spectral_r',edgecolor='black',linewidth=1.5)
plt.title('Most Popular Outlets in Bangalore')
plt.xlabel('Number of Counts')

Text(0.5, 0, 'Number of Counts')


locations = pd.DataFrame({'Name':d1['location'].unique()})              # Creating Latitude & Longitude.

locations['New_Name'] = 'Bangalore' + locations['Name']

lat_lon = []   # list that will store tha lat and lon.
geolocator = Nominatim(user_agent = 'app')    # to establish Connection
for location in locations['Name']:
    location = geolocator.geocode(location)
    if location is None:
        lat_lon.append(np.nan)   # if no location is there
    else:
        geo = (location.latitude,location.longitude)
        lat_lon.append(geo)
        
locations['geo_loc'] = lat_lon

Rest_location = pd.DataFrame(d1['location'].value_counts().reset_index())  # this is used to plot maps.
Rest_location.columns=['Name','Count']

Restaurant_locations = Rest_location.merge(locations,on='Name',how='left').dropna()

def GeneratBaseMap(default_location = [12.93,77.61], default_zoom_start = 12):
    BaseMap = folium.Map(location = default_location,zoom_start = default_zoom_start)
    return BaseMap
np.array(Restaurant_locations['geo_loc'] )

lat,lon = zip(*np.array(Restaurant_locations['geo_loc'])) # unziping it.


Restaurant_locations['lat'] = lat
Restaurant_locations['lon'] = lon
Restaurant_locations.head()


BaseMap = GeneratBaseMap()                         # This the Base Map in Bangalore.
BaseMap


Restaurant_locations[['lat','lon','Count']].values.tolist()   # To Count How Many Resturant are there.

HeatMap(Restaurant_locations[['lat','lon','Count']].values.tolist(),zoom=20,radius=15).add_to(BaseMap)
BaseMap     # Creating a HeatMap.


d1_ = d1[d1['cuisines'] == 'North Indian']                                      # Heat Map of North Indian Restaurant.
North_indian = d1_.groupby(['location'],as_index = False)['url'].agg('count')
North_indian.columns = ['Name','Count']
North_indian = North_indian.merge(locations,on='Name',how='left').dropna()
North_indian['lan'],North_indian['lon']=zip(*North_indian['geo_loc'].values)
North_indian.drop(['geo_loc'],axis=1)
North_indian.head()


BaseMap = GeneratBaseMap()                         # This the Base Map of North Indian Restaurant.
HeatMap(North_indian[['lan','lon','Count']].values.tolist(),zoom=20,radius=15).add_to(BaseMap)
BaseMap


d1_ = d1[d1['cuisines'] == 'South Indian']                                 # Heat Map of South Indian Restaurant.
South_indian = d1_.groupby(['location'],as_index = False)['url'].agg('count')
South_indian.columns = ['Name','Count']
South_indian = South_indian.merge(locations,on='Name',how='left').dropna()
South_indian['lan'],South_indian['lon']=zip(*South_indian['geo_loc'].values)
South_indian = South_indian.drop(['geo_loc'],axis=1)
South_indian.head()


BaseMap = GeneratBaseMap()                         # This the Base Map of North Indian Restaurant.
HeatMap(South_indian[['lan','lon','Count']].values.tolist(),zoom=20,radius=15).add_to(BaseMap)
BaseMap


d1.head()


O_r1 = d1['online_order'].value_counts(ascending = False)   # This Indicates Online_order Facility.
O_r1

Yes    30311
No     20837
Name: online_order, dtype: int64


plt.figure(figsize= (12,5))                             # This Graph Showing Online_order Facility.
plt.xticks(fontsize=14)
sns.countplot(d1['online_order'],edgecolor='black',linewidth=2)
plt.title('This Graph Indicates Online Order Facility Only')

C:\Users\Sabae\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

Text(0.5, 1.0, 'This Graph Indicates Online Order Facility Only')


O_r2 = d1.groupby(['restaurant','online_order'])['restaurant'].count()   # In Which Restaurant Online Order Facility Avilable.
O_r2.to_csv('restaurant_online.csv')
O_r2 = pd.read_csv('restaurant_online.csv')
O_r2 = pd.pivot_table(O_r2,values = None,index = ['restaurant'], columns = ['online_order'],
                      fill_value=0,aggfunc=np.sum)[8:50:3]
O_r2


O_r3 = ['11 to 11 Express Biriyanis','1441 Pizzeria','1722 Urban Bistro','1980s Games Cafe','1TO3 Kitchen','20 Feet High','22S','24 Hours Coffee Drop - La Classic','24@43 - The Oterra','2nd Home Restaurant','30 Square','37 China Town','3Gs - Gulp & Groove','4 Mangoes']
y1 = [0,0,0,0,1,0,0,2,2,0,0,2,0,2]
y2 = [2,6,11,5,0,19,1,0,0,11,11,0,8,0]


plt.figure(figsize= (18,5))                 # In Which Restaurant Online Order Facility is Avilable or Not.
plt.xticks(rotation=90,fontsize=16)
plt.title('Online Order Wise Restaurant Graph')
plt.xlabel('Restaurant')
plt.ylabel('Online_Order')
plt.bar(O_r3,y1,color='r',edgecolor='black',linewidth=1.5)
plt.bar(O_r3,y2,bottom=y1,color='y',edgecolor='black',linewidth=1.5)
plt.legend(['Avilable','Not Avilable'])
plt.show()


O_r4 = d1.groupby('rate').count()
O_r5 = O_r4[['online_order']]
O_r5[0:7:4]


Online_order = ['5','51','303','1550','2764','3819','2155','300']
Rating = [1.8,2.3,2.7,3.1,3.5,3.8,4.2,4.6]


plt.figure(figsize= (18,5))                 # Online Order Wise Rating Data.
plt.xticks(rotation=0,fontsize=16)
plt.title('Online Order Wise Rating Graph')
plt.barh(Online_order,Rating,edgecolor='black',linewidth=2,color=['#67678f', '#f59642', '#bcf542', '#4287f5', '#9edbdb','#f54287','#f542da','lightgreen'])
plt.xlabel('Rating')
plt.ylabel('Online Order')

Text(0, 0.5, 'Online Order')


d1.head()


B_t1 = d1['book_table'].value_counts(ascending = False)   # This Indicates Book_Table Facility. 
B_t1

No     44699
Yes     6449
Name: book_table, dtype: int64


plt.figure(figsize= (12,5))                             
plt.xticks(fontsize=14)                             # This Graph Showing Book_Table Facility.
ax = sns.countplot(d1['book_table'],edgecolor='black',palette='GnBu_r',linewidth=1.5)
plt.title('This Graph Indicates Book Table Facility Only')

C:\Users\Sabae\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

Text(0.5, 1.0, 'This Graph Indicates Book Table Facility Only')


B_t2 = d1.groupby(['restaurant','book_table'])['restaurant'].count()   # In Which Restaurant Online Order Facility Avilable.
B_t2.to_csv('restaurant_online.csv')
B_t2 = pd.read_csv('restaurant_online.csv')
B_t2 = pd.pivot_table(B_t2,values = None,index = ['restaurant'], columns = ['book_table'],
                      fill_value=0,aggfunc=np.sum)[3:50:4]
B_t2


B_t3 = ['#refuel','ÃÂÃÂ°C','1441 Pizzeria','18+ Ice Cafe','1Q1','20 Feet High','24 Carats - The Capitol','24/7 Food Service','2nd Home Restaurant','360 Atoms Restaurant And Cafe','3Bs - Buddies, Bar & Barbecues','4 Mangoes']
x1 = [3,3,6,6,0,0,12,4,11,7,0,2]
x2 = [5,0,10,0,16,19,0,8,0,0,17,0]


len(x2)

12


plt.figure(figsize= (18,5))                 # In Which Restaurant Book Table or Not.
plt.xticks(rotation=90,fontsize=16)
plt.title('Book Table Wise Restaurant Graph')
plt.xlabel('Restaurant')
plt.ylabel('Book_Table')
plt.bar(B_t3,x1,color='#7c89de',edgecolor='black',linewidth=1.5)
plt.bar(B_t3,x2,bottom=x1,color='#9aedd7',edgecolor='black',linewidth=1.5)
plt.legend(['Book','Not Book'])
plt.show()

C:\Users\Sabae\anaconda3\lib\site-packages\IPython\core\pylabtools.py:151: UserWarning:

Glyph 131 (\x83) missing from current font.

C:\Users\Sabae\anaconda3\lib\site-packages\IPython\core\pylabtools.py:151: UserWarning:

Glyph 130 (\x82) missing from current font.


plt.figure(figsize= (15,13))
sns.boxplot(data=d1,x='book_table',y='rate',palette='PuBuGn_r') 
plt.legend(['Yes','No'])

<matplotlib.legend.Legend at 0x1fc73c408b0>


d1.head()


d1[['rate','cost_two_plates','online_order']]


plt.figure(figsize= (10,7))                          # Ploting Scatter plot.
plt.title('Rated Wise Online Order Graph')
sns.scatterplot(x='rate',y='cost_two_plates',hue='online_order',data=d1)
plt.show()


d1['rate'].value_counts().head(10)

3.700449    9885
3.900000    3958
3.800000    3819
3.700000    3808
3.600000    3287
4.000000    3145
4.100000    2928
3.500000    2764
3.400000    2447
3.300000    2277
Name: rate, dtype: int64


fig = px.histogram(x='rate',data_frame=d1)                       # Ploting histogram graph.
fig.update_traces(marker_line_width=2,marker_line_color='black')


Rate1 = d1.groupby(['rate','book_table'])['rate'].count()   # Rating Wise Book Table.
Rate1.to_csv('rate_online.csv')
Rate1 = pd.read_csv('rate_online.csv')
Rate1 = pd.pivot_table(Rate1,values = None,index = ['rate'], columns = ['book_table'],
                      fill_value=0,aggfunc=np.sum)[1:45:3]
Rate1


Rate2 = ['1.8','2.2','2.5','2.8','3.1','3.4','3.7','3.9','4.2','4.5','4.8']
Y1 = [5,13,98,574,1540,2431,3590,3401,1204,233,23]
N2 = [0,13,2,6,10,16,218,557,951,423,43]


plt.figure(figsize= (10,5))                 # Online Order Wise Rating Data.
plt.xticks(rotation=0,fontsize=12)
plt.title('Rating Wise Book Table Graph')
plt.xlabel('Book Table')
plt.ylabel('Rating')
plt.barh(Rate2,Y1,color='w',edgecolor='black',linewidth=1.5)
plt.barh(Rate2,N2,color='k',edgecolor='black',linewidth=1.5)
plt.legend(['No','Yes'])
plt.show()


d1.head()


Restaurant = []
Location = []
for key,location in d1.groupby('location'):
    Location.append(key)
    Restaurant.append(len(location['restaurant'].unique()))


Location1 = pd.DataFrame(zip(Location,Restaurant))                       #  Location Wise Data Extract.
Location1.columns = ['Location','Restaurant']
Location1.set_index('Location',inplace=True)
Loc = Location1.sort_values(by='Restaurant',ascending= False)[1:30:3]
Loc


colors = ['gold', 'mediumturquoise', 'darkorange', 'lightgreen','dark','lightblack','lightblue','darkyellow','light','lightdeep']

fig = go.Figure(data=[go.Pie(labels=['BTM','Marathahalli','Bannerghatta Road','Sarjapur Road','Koramangala 5th Block','Rajajinagar','Frazer Town','Koramangala 1st Block','Old Airport Road','Ulsoor'],
                             values=[695,651,445,321,263,226,188,166,135,117])])
fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=15,
                  marker=dict(colors=colors, line=dict(color='#000000', width=1.5)))
fig.update_layout(
       title='Locatio Wise Restaurant Pie Chart',
       legend_title='Location')
fig.show()


Loc1 = d1['location'].value_counts().rename_axis('Location').reset_index(name='Resturant')[:20]   # Most Famous Foodies area in Bangalore.
Loc1


fig = plt.figure(figsize=(15,5))               # ploting Barplot.
plt.xticks(rotation=90,fontsize=12)
sns.set_style(style='whitegrid')            
sns.barplot(data=Loc1,x='Location',y='Resturant',palette='twilight_shifted_r',edgecolor='black')
plt.title('Most Famous Foodies Location in Bangalore')

Text(0.5, 1.0, 'Most Famous Foodies Location in Bangalore')


Loc2 = d1.groupby('location')['restaurant'].unique().rename_axis('Location').reset_index(name='Restaurant Name')
Loc2


Loc3 = d1['location'].value_counts(ascending = False).rename_axis('Location').reset_index(name='Resturant').head(10)
Loc3                                      # Max Restaurant Location.


X_data = ['BTM','HSR','Koramangala 5th Block','JP Nagar','Whitefield','Indiranagar','Jayanagar','Marathahalli','Bannerghatta Road','Others']

Y_data = [5071,4962,2496,2481,2219,2109,2033,1916,1808,1611]


x_data = ['St. Marks Road','Commercial Street','Ejipura','Old Airport Road','Domlur','Cunningham Road','Shivajinagar','Shanti Nagar','Lavelle Road','Church Street']

y_data = [904,894,841,804,724,720,684,674,656,645]


fig = plt.figure(figsize=(17,4))
plt.subplot(1,2,1)
plt.bar(X_data,Y_data,color='c',edgecolor='black',linewidth=2)
plt.xlabel('Location')
plt.ylabel('Restaurant')
plt.legend(['Maximum'])
plt.xticks(rotation=90,fontsize=14)
plt.title('Location Wise Max Restaurant')
plt.subplot(1,2,2)
plt.bar(x_data,y_data,color='y',edgecolor='black',linewidth=2)
plt.xlabel('Location')
plt.ylabel('Restaurant')
plt.legend(['Minimum'])
plt.xticks(rotation=90,fontsize=14)
plt.title('Location Wise Min Restaurant')

Text(0.5, 1.0, 'Location Wise Min Restaurant')


Loc4 = d1['location'].value_counts(ascending = True).rename_axis('Location').reset_index(name='Resturant').head(10)
Loc4                                          # Min Restaurant Location.


Loc5 = d1.groupby(['location','online_order'])['restaurant'].count()   
Loc5.to_csv('location_online.csv')
Loc5 = pd.read_csv('location_online.csv')
Loc5 = pd.pivot_table(Loc5,values=None, index=['location'],
                      columns=['online_order'], fill_value=0, aggfunc=np.sum)
Loc5.head(10)


Location = ['BTM','Banashankari','Banaswadi','Bannerghatta Road','Basavanagudi','Bellandur','Brigade Road','Brookefield','Church Street','Commercial Street']
No = [1766,397,302,687,243,519,553,239,226,228]
Yes = [3305,507,343,924,441,752,665,417,343,142]


plt.figure(figsize= (18,7))                 # ploting Bar Plot.
plt.xticks(rotation=90,fontsize=16)
plt.title('Location Wise Online Order Facility Graph')
plt.bar(Location,No,color='b',edgecolor='black',linewidth=2)
plt.bar(Location,Yes,bottom=No,color='r',edgecolor='black',linewidth=2)
plt.xlabel('Location')
plt.ylabel('Online_Order')
plt.legend(['Facility','No Facility'])
plt.show()


Loc6 = d1.groupby(['location','book_table'])['restaurant'].count()   # Book Table Wise Data Extract.
Loc6.to_csv('location_book.csv')
Loc6 = pd.read_csv('location_book.csv')
Loc6 = pd.pivot_table(Loc6,values=None, index=['location'], 
                      columns=['book_table'], fill_value=0,
                      aggfunc=np.sum)[10:70:3]
Loc6


LOCATION = ['Cunningham Road','Electronic City','Indiranagar','Kalyan Nagar','Koramangala 4th Block','Koramangala 7th Block','Malleshwaram','Old Airport Road','Residency Road','Shanti Nagar','Ulsoor']
NO = [316,1149,1579,692,695,1012,635,378,523,453,840]
YES = [175,99,454,149,322,164,89,59,151,58,177]


plt.figure(figsize= (18,7))                 # ploting Bar Plot.
plt.xticks(rotation=90,fontsize=16)
plt.title('Location Wise Book Table Graph')
plt.bar(LOCATION,NO,color='coral',edgecolor='black',linewidth=2)
plt.bar(LOCATION,YES,bottom=NO,color='antiquewhite',edgecolor='black',linewidth=2)
plt.xlabel('Location')
plt.ylabel('Book_Table')
plt.legend(['Booked','No Booked'])
plt.show()


Loc7 = d1.groupby(['location']).sum() 
Loc8 = Loc7.sort_values('votes',ascending=False)   # Location Wise Highest Votes.
Loc9 = Loc8[['votes']].head(10)
Loc9


plt.figure(figsize=(15,8))              # Location Wise Highest Votes.
sns.barplot(Loc9.index ,Loc9['votes'],edgecolor='black',palette='magma_r',linewidth=2)
plt.xticks(rotation=45)

C:\Users\Sabae\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

(array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
 [Text(0, 0, 'Koramangala 5th Block'),
  Text(1, 0, 'Indiranagar'),
  Text(2, 0, 'Koramangala 4th Block'),
  Text(3, 0, 'Church Street'),
  Text(4, 0, 'JP Nagar'),
  Text(5, 0, 'BTM'),
  Text(6, 0, 'Lavelle Road'),
  Text(7, 0, 'HSR'),
  Text(8, 0, 'Koramangala 7th Block'),
  Text(9, 0, 'Jayanagar')])


d1.head()


Rest1 = (d1['rest_type'].value_counts()/len(d1)*100).rename_axis('rest_type').reset_index(name='Counts').head(15)
Rest1


fig = plt.figure(figsize=(15,5))
plt.xticks(rotation=90,fontsize=14)
sns.set_style(style='whitegrid')            # Differnt Types of Restaurant in Bangalore.
sns.barplot(data=Rest1,x='rest_type',y='Counts',palette='turbo',edgecolor='black')
plt.title('Differnt Types of Restaurant in Bangalore')

Text(0.5, 1.0, 'Differnt Types of Restaurant in Bangalore')


d1['rest_type']

0              Casual Dining
1              Casual Dining
2        Cafe, Casual Dining
3                Quick Bites
4              Casual Dining
                ...         
51712                    Bar
51713                    Bar
51714                    Bar
51715                    Bar
51716     Bar, Casual Dining
Name: rest_type, Length: 51148, dtype: object


fig = px.histogram(x='rest_type',data_frame=d1)                   # ploting histogram.
fig.update_traces(marker_line_width=2,marker_line_color='black')


Rest2 = d1['rest_type'].value_counts().rename_axis('rest_type').reset_index(name='Counts').head(15)
Rest2


plt.figure(figsize= (17,10))                                # ploting Barplot.
sns.set_style(style='white')
Chains = d1['rest_type'].value_counts().head(15)
sns.barplot(x=Chains,y=Chains.index,palette='gist_rainbow_r',edgecolor='black',linewidth=2)
plt.xticks(fontsize=10)
plt.title('Top 15 Best Restaurant in Bnagalore')
plt.ylabel('Rest_Type')
plt.xlabel('Number of Counts')

Text(0.5, 0, 'Number of Counts')


Rest4 = d1.groupby('rate')['rest_type'].unique().rename_axis('Rate').reset_index(name='Rest_Type Name')[0:50:5]
Rest4


plt.figure(figsize= (9,9))                          # ploting Circle Pie Chart
plt.xticks(fontsize=45)
labels = ['Casual Dining','Quick Bites','Dessert Parlor','Bakery','Cafe','Takeaway','Bakery, Cafe']
sizes = [1.8,2.4,2.9,3.4,3.8,4.3,4.8]
colors = ['#f76516', '#9516f7', '#cfe605', '#16f725','#FFA500','#32a4a8','#f0565b']
explode = (0.0,0.0,0.0,0.0,0.0,0.0,0.05)
plt.pie(sizes, colors=colors,labels=labels,wedgeprops={'edgecolor':'k'},
        autopct='%1.1f%%', pctdistance=0.70,
        explode=explode)
centre_circle = plt.Circle((0, 0), 0.40, fc='white',edgecolor='black',linewidth=2)
fig = plt.gcf()
fig.gca().add_artist(centre_circle)
plt.title('Location Wise Highest & Lowest Rating')
plt.legend(sizes, loc="upper right", title="Fruits Color",fontsize=7)
plt.show()


d1.head()


d1['cuisines'].value_counts()[:10]

North Indian                           2858
North Indian, Chinese                  2355
South Indian                           1822
Biryani                                 906
Bakery, Desserts                        899
Fast Food                               798
Desserts                                760
Cafe                                    726
South Indian, North Indian, Chinese     724
Bakery                                  649
Name: cuisines, dtype: int64


plt.figure(figsize= (10,6))                         # ploting Barplot.
plt.xticks(fontsize=10)
Cusin1 = d1['cuisines'].value_counts()[:10]
sns.barplot(Cusin1,Cusin1.index,edgecolor='black')
plt.xlabel('Count')
plt.title('Most Popular Cuisines of Bangalore')

C:\Users\Sabae\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

Text(0.5, 1.0, 'Most Popular Cuisines of Bangalore')


Cusin2 = d1.groupby('cuisines')['restaurant'].unique().rename_axis('cuisines').reset_index(name='Restaurant Name')
Cusin2


Cusin3 = d1.groupby(['cuisines']).sum() 
Cusin4 = Cusin3.sort_values('votes',ascending=False)   # Cuisines Wise Highest Votes.
Cusin5 = Cusin4[['votes']].head(15)
Cusin5


plt.figure(figsize=(18,8))             # Cuisines Wise Highest Votes.
plt.xticks(fontsize=13,rotation=90)
sns.barplot(Cusin5.index ,Cusin5['votes'],edgecolor='black',palette='turbo',linewidth=2)

C:\Users\Sabae\anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

<AxesSubplot:xlabel='cuisines', ylabel='votes'>


Cusin6 = d1.groupby(['cuisines','online_order'])['online_order'].count()           # Cuisines Wise Data Extract.
Cusin6.to_csv('cuisines_online.csv')
Cusin6 = pd.read_csv('cuisines_online.csv')
Cusin6 = pd.pivot_table(Cusin6,values=None, index=['cuisines'],
                      columns=['online_order'], fill_value=0, aggfunc=np.sum)
Cusin6


Cuisines = ['Vietnamese','Turkish, Rolls','American, Asian, Continental, North Indian, South Indian, Chinese','African, Burger','American, Asian, European, North Indian','American','Vietnamese, Thai, Burmese, Japanese','African, Burger, Desserts, Beverages, Fast Food','Vietnamese, Salad','Turkish, Fast Food, Biryani, Chinese']
online_order = [19,17,12,11,8,5,4,2,0,0]


plt.figure(figsize= (10,5))                    # ploting Bar Graph.
plt.xticks(rotation=90,fontsize=13)
plt.title('Location Wise Book Table Graph')
plt.bar(Cuisines,online_order,color='c',edgecolor='black',linewidth=2)
plt.xlabel('Cuisines')
plt.ylabel('Online Order')

Text(0, 0.5, 'Online Order')


Cusin7 = d1.groupby('rate')['cuisines'].unique().rename_axis('Rate').reset_index(name='Cuisines Name')[0:50:5]
Cusin7


Cuisines = ['North Indian, Continental, Chinese','North Indian, North Indian, Chinese, Thai','Chinese, Continental, Italian, North Indian','Biryani, North Indian, Chinese, Fast Food','Cafe, Mexican, Italian, North Indian, Rajasth','Ice Cream, Desserts, North Indian, Street Food','South Indian, North Indian, European, Mediter']
Rate = [1.8,2.4,2.9,3.4,3.8,4.3,4.8]


plt.figure(figsize= (10,5))                             # ploting Bar Graph.
plt.xticks(rotation=90,fontsize=13)
plt.title('Location Wise Book Table Graph')
plt.bar(Cuisines,Rate,color='#7B68EE',edgecolor='black',linewidth=3)
plt.xlabel('Cuisines')
plt.ylabel('Online Order')

Text(0, 0.5, 'Online Order')


d1.head()


d1['cost_two_plates'].value_counts()

300.0     7549
400.0     6517
500.0     4937
200.0     4844
600.0     3691
          ... 
60.0         1
5000.0       1
3700.0       1
469.0        1
160.0        1
Name: cost_two_plates, Length: 70, dtype: int64


fig = px.histogram(x='cost_two_plates',data_frame=d1)                 # ploting histogram.
fig.update_traces(marker_line_width=2,marker_line_color='black')


d1['cost_two_plates']/2

0         400.0
1         400.0
2         400.0
3         150.0
4         300.0
          ...  
51712     750.0
51713     300.0
51714    1000.0
51715    1250.0
51716     750.0
Name: cost_two_plates, Length: 51148, dtype: float64


d1['cost_two_plates'].nlargest(10)

19139    6000.0
45618    6000.0
40266    5000.0
41591    4500.0
42141    4500.0
37715    4100.0
39125    4100.0
39416    4100.0
40262    4100.0
6104     4000.0
Name: cost_two_plates, dtype: float64


fig = plt.figure(figsize=(17,4))                  # ploting Subplot.
plt.subplot(1,2,1)
d1['cost_two_plates'].nlargest(10).plot.bar()
plt.xlabel('Count')
plt.ylabel('Cost Two Plates')
plt.legend(['Maximum'])
plt.xticks(fontsize=12)
plt.title('Top 10 Largest Restaurant for Cost Two People')
plt.subplot(1,2,2)
d1['cost_two_plates'].nsmallest(10).plot.bar()
plt.xlabel('Count')
plt.ylabel('Cost Two Plates')
plt.legend(['Minimum'])
plt.xticks(fontsize=12)
plt.title('Top 10 Smallest Restaurant for Cost Two People')

Text(0.5, 1.0, 'Top 10 Smallest Restaurant for Cost Two People')


d1['cost_two_plates'].nsmallest(10)

5270     40.0
12232    40.0
14819    40.0
17773    40.0
18891    40.0
27091    40.0
29785    40.0
32485    40.0
1604     50.0
1892     50.0
Name: cost_two_plates, dtype: float64


Cost1 = d1[d1['cost_two_plates'] <= 500].loc[:,('cost_two_plates')]
Cost2 = Cost1.reset_index()
Cost2.head()


Cost4 = go.Bar(x=Cost2['cost_two_plates'].value_counts().index,           # ploting Bar graph.
                y=Cost2['cost_two_plates'].value_counts())
iplot([Cost4])


d1.head()


d1['reviews_list'][0]                         # Cleaning Review Column With The Help of Regex.
data = d1['reviews_list'][0].lower()
data2 = re.sub('[^a-zA-Z]',' ',data)
data3 = re.sub('rated',' ',data2)
data4 = re.sub('x',' ',data3)
re.sub(' +',' ',data4)

' n a beautiful place to dine in the interiors take you back to the mughal era the lightings are just perfect we went there on the occasion of christmas and so they had only limited items available but the taste and service was not compromised at all the only complaint is that the breads could have been better would surely like to come here again n i was here for dinner with my family on a weekday the restaurant was completely empty ambience is good with some good old hindi music seating arrangement are good too we ordered masala papad panner and baby corn starters lemon and corrionder soup butter roti olive and chilli paratha food was fresh and good service is good too good for family hangout ncheers n its a restaurant near to banashankari bda me along with few of my office friends visited to have buffet but unfortunately they only provide veg buffet on inquiring they said this place is mostly visited by vegetarians anyways we ordered ala carte items which took ages to come food was ok ok definitely not visiting anymore n we went here on a weekend and one of us had the buffet while two of us took ala carte firstly the ambience and service of this place is great the buffet had a lot of items and the good was good we had a pumpkin halwa intm the dessert which was amazing must try the kulchas are great here cheers n the best thing about the place is it s ambiance second best thing was yummy food we try buffet and buffet food was not disappointed us ntest nquality nservice staff was very professional and friendly n noverall e perience was e cellent n nsubirmajumder wi site com n great food and pleasant ambience e pensive but coll place to chill and rela n nservice is really very very good and friendly staff n nfood nservice nambience noverall n good ambience with tasty food ncheese chilli paratha with bhutta palak methi curry is a good combo nlemon chicken in the starters is a must try item negg fried rice was also quite tasty nin the mocktails recommend alice in junoon do not miss it n you can t go wrong with jalsa never been a fan of their buffet and thus always order alacarte service at times can be on the slower side but food is worth the wait n overdelighted by the service and food provided at this place a royal and ethnic atmosphere builds a strong essence of being in india and also the quality and taste of food is truly authentic i would totally recommend to visit this place once n the place is nice and comfortable food wise all jalea outlets maintain a good standard the soya chaap was a standout dish clearly one of trademark dish as per me and a must try n nthe only concern is the parking it very congested and limited to just cars the basement parking is very steep and makes it cumbersome n the place is nice and comfortable food wise all jalea outlets maintain a good standard the soya chaap was a standout dish clearly one of trademark dish as per me and a must try n nthe only concern is the parking it very congested and limited to just cars the basement parking is very steep and makes it cumbersome n the place is nice and comfortable food wise all jalea outlets maintain a good standard the soya chaap was a standout dish clearly one of trademark dish as per me and a must try n nthe only concern is the parking it very congested and limited to just cars the basement parking is very steep and makes it cumbersome '


d1['rest_type'].unique()

array(['Casual Dining', 'Cafe, Casual Dining', 'Quick Bites',
       'Casual Dining, Cafe', 'Cafe', 'Quick Bites, Cafe',
       'Cafe, Quick Bites', 'Delivery', 'Mess', 'Dessert Parlor',
       'Bakery, Dessert Parlor', 'Pub', 'Bakery', 'Takeaway, Delivery',
       'Fine Dining', 'Beverage Shop', 'Sweet Shop', 'Bar',
       'Beverage Shop, Quick Bites', 'Confectionery',
       'Quick Bites, Beverage Shop', 'Dessert Parlor, Sweet Shop',
       'Bakery, Quick Bites', 'Sweet Shop, Quick Bites', 'Kiosk',
       'Food Truck', 'Quick Bites, Dessert Parlor',
       'Beverage Shop, Dessert Parlor', 'Takeaway', 'Pub, Casual Dining',
       'Casual Dining, Bar', 'Dessert Parlor, Beverage Shop',
       'Quick Bites, Bakery', 'Dessert Parlor, Quick Bites',
       'Microbrewery, Casual Dining', 'Lounge', 'Bar, Casual Dining',
       'Food Court', 'Cafe, Bakery', 'Dhaba', 'Quick Bites, Sweet Shop',
       'Microbrewery', 'Food Court, Quick Bites', 'Pub, Bar',
       'Casual Dining, Pub', 'Lounge, Bar', 'Food Court, Dessert Parlor',
       'Casual Dining, Sweet Shop', 'Food Court, Casual Dining',
       'Casual Dining, Microbrewery', 'Sweet Shop, Dessert Parlor',
       'Bakery, Beverage Shop', 'Lounge, Casual Dining',
       'Cafe, Food Court', 'Beverage Shop, Cafe', 'Cafe, Dessert Parlor',
       'Dessert Parlor, Cafe', 'Dessert Parlor, Bakery',
       'Microbrewery, Pub', 'Bakery, Food Court', 'Club',
       'Quick Bites, Food Court', 'Bakery, Cafe', 'Bar, Cafe',
       'Pub, Cafe', 'Casual Dining, Irani Cafee', 'Fine Dining, Lounge',
       'Bar, Quick Bites', 'Bakery, Kiosk', 'Pub, Microbrewery',
       'Microbrewery, Lounge', 'Fine Dining, Microbrewery',
       'Fine Dining, Bar', 'Mess, Quick Bites', 'Dessert Parlor, Kiosk',
       'Bhojanalya', 'Casual Dining, Quick Bites', 'Pop Up', 'Cafe, Bar',
       'Casual Dining, Lounge', 'Bakery, Sweet Shop', 'Microbrewery, Bar',
       'Cafe, Lounge', 'Bar, Pub', 'Lounge, Cafe', 'Club, Casual Dining',
       'Quick Bites, Mess', 'Quick Bites, Meat Shop',
       'Quick Bites, Kiosk', 'Lounge, Microbrewery',
       'Food Court, Beverage Shop', 'Dessert Parlor, Food Court',
       'Bar, Lounge'], dtype=object)


stopwords = set(STOPWORDS)


Review1 = d1[d1['rest_type'] == 'Casual Dining'].head(10)           # Creating WordCloud.

total_review = ''

# Converts each token into lowercase
for review in Review1['reviews_list']:
    review = review.lower()
    review = re.sub('^a-zA-z',' ',review)
    review = re.sub('rated',' ',review)
    review = re.sub('x',' ',review)
    review = re.sub(' +',' ',review)
    
    # typecaste each val to string
    total_review = total_review + str(review)
    
    
wordcloud = WordCloud(width = 700, height = 300,random_state=123,
                 background_color = 'black',colormap='Set2',collocations=False,
                     stopwords = stopwords,
                     min_font_size = 10).generate(total_review)

# plot the WordCloud image  
plt.figure(figsize= (8,8))
plt.imshow(wordcloud)
plt.axis('off')
plt.tight_layout(pad = 0)

plt.show()


Review1 = d1[d1['rest_type'] == 'Mess'].head(10)                            # Creating WordCloud.

total_review = ''

# Converts each token into lowercase
for review in Review1['reviews_list']:
    review = review.lower()
    review = re.sub('^a-zA-z',' ',review)
    review = re.sub('rated',' ',review)
    review = re.sub('x',' ',review)
    review = re.sub(' +',' ',review)
    
    # typecaste each val to string
    total_review = total_review + str(review)
    
    
wordcloud = WordCloud(width = 900, height = 400,random_state=123,
                 background_color = 'white',colormap='turbo',collocations=False,
                     stopwords = stopwords,
                     min_font_size = 10).generate(total_review)

# plot the WordCloud image  
plt.figure(figsize= (8,8))
plt.imshow(wordcloud)
plt.axis('off')
# plt.tight_layout(pad = 0)

plt.show()


Review1 = d1[d1['rest_type'] == 'Quick Bites'].head(10)                 # Creating WordCloud.

total_review = ''

# Converts each token into lowercase
for review in Review1['reviews_list']:
    review = review.lower()
    review = re.sub('^a-zA-z',' ',review)
    review = re.sub('rated',' ',review)
    review = re.sub('x',' ',review)
    review = re.sub(' +',' ',review)
    
    # typecaste each val to string
    total_review = total_review + str(review)
    
wordcloud = WordCloud(width = 700, height = 300,random_state=123,
                 background_color = 'purple',colormap='YlOrBr_r',collocations=False,
                     stopwords = stopwords,
                     min_font_size = 10).generate(total_review)

# plot the WordCloud image  
plt.figure(figsize= (8,8))
plt.imshow(wordcloud)
plt.axis('off')
plt.tight_layout(pad = 0)

plt.show()


from PIL import Image
Review1 = d1[d1['rest_type'] == 'Beverage Shop'].head(10)             # Creating WordCloud.

total_review = ''

# Converts each token into lowercase
for review in Review1['reviews_list']:
    review = review.lower()
    review = re.sub('^a-zA-z',' ',review)
    review = re.sub('rated',' ',review)
    review = re.sub('x',' ',review)
    review = re.sub(' +',' ',review)
    
    # typecaste each val to string
    total_review = total_review + str(review)
    
wordcloud = WordCloud(width = 700, height = 300,random_state=123,
                 background_color ='white',collocations=False,
                     stopwords = stopwords,
                     min_font_size = 10).generate(total_review)

# plot the WordCloud image  
plt.figure(figsize= (8,8))
plt.imshow(wordcloud)
plt.axis('off')
plt.tight_layout(pad = 0)

plt.show()


Original_data.head()


Original_data = Original_data.drop(['address','phone','listed_in(city)','menu_item'], axis=1)

Original_data.drop_duplicates(inplace= True)



def Cleaning_rate(cl_rate):               # Creating Function to Clean Rate Column.
    if(cl_rate == 'NEW' or cl_rate == '-'):               # Removing 'NEW','-' & '/5'
        return np.nan
    else:
        return float(str(cl_rate).split('/')[0])
        
Original_data['rate'] = Original_data['rate'].apply(Cleaning_rate)



Original_data['rate'].fillna(d1['rate'].mean(), inplace= True)   # Filing Null Values.

Original_data.dropna(inplace= True)

Original_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 23435 entries, 0 to 51715
Data columns (total 13 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   url                          23435 non-null  object 
 1   name                         23435 non-null  object 
 2   online_order                 23435 non-null  object 
 3   book_table                   23435 non-null  object 
 4   rate                         23435 non-null  float64
 5   votes                        23435 non-null  int64  
 6   location                     23435 non-null  object 
 7   rest_type                    23435 non-null  object 
 8   dish_liked                   23435 non-null  object 
 9   cuisines                     23435 non-null  object 
 10  approx_cost(for two people)  23435 non-null  object 
 11  reviews_list                 23435 non-null  object 
 12  listed_in(type)              23435 non-null  object 
dtypes: float64(1), int64(1), object(11)
memory usage: 2.5+ MB


Rest = Original_data['rest_type'].value_counts()[:9].index

Data = Original_data[Original_data['rest_type'] == 'Quick Bites']

stopwords = set(STOPWORDS)



dishes = ''
for word in Data['dish_liked']:
    words = word.split()
    for i in range(len(words)):         # Converting each token into uppercase.
        words[i] = words[i].lower()
    dishes = dishes + " ".join(words)+" "
    
    
mask = np.array(Image.open('Comment.png'))

wordcloud = WordCloud(width = 700, height = 300,random_state=123,
                 background_color = 'purple',colormap='Set2',collocations=False,mask=mask,
                     stopwords = stopwords,
                     min_font_size = 10).generate(dishes)



plt.imshow(wordcloud)
plt.axis("off")

(-0.5, 2047.5, 2047.5, -0.5)


Rest = Original_data['rest_type'].value_counts()[:9].index

Data = Original_data[Original_data['rest_type'] == 'Quick Bites']

stopwords = set(STOPWORDS)


def produce_wordcloud(rest):
    
    plt.figure(figsize= (20,30))
    for i,restaurant in enumerate(Rest):
        plt.subplot(3,3,i+1)
        dishes = ''
        data = Original_data[Original_data['rest_type'] == restaurant]
        for word in Data['dish_liked']:
            words = word.split()
            for i in range(len(words)):
                words[i] = words[i].lower()
            dishes = dishes + " ".join(words) + " "
        wordcloud = WordCloud(max_font_size=None,background_color='black', collocations=False,
                      stopwords = stopwords,width=1500, height=1500).generate(dishes)
        plt.imshow(wordcloud)
        plt.title(restaurant)
        plt.axis("off")
                
                    
                
        
stopwords = set(STOPWORDS)
produce_wordcloud(Rest)


d1.head()


d1['type'].unique()

array(['Buffet', 'Cafes', 'Delivery', 'Desserts', 'Dine-out',
       'Drinks & nightlife', 'Pubs and bars'], dtype=object)


Type1 = d1['type'].value_counts().rename_axis('Type').reset_index(name='Counts')
Type1


fig = plt.figure(figsize=(15,5))                     # ploting Barplot.
plt.xticks(fontsize=13)
sns.set_style(style='whitegrid')
sns.barplot(data=Type1,x='Type',y='Counts',palette='turbo',edgecolor='black',linewidth=1.5)
plt.title('Most Famous Restaurant Where Customer Visit Most')

Text(0.5, 1.0, 'Most Famous Restaurant Where Customer Visit Most')


Type2 = d1.groupby('rate')['type'].unique().rename_axis('Rate').reset_index(name='Type Name')[0:50:5]
Type2


Type = ['Buffet','Cafes','Delivery','Desserts','Dine-out','Drinks & nightlife','Pubs and bars']
Rating = [1.8,2.4,2.9,3.4,3.8,4.3,4.8]


plt.figure(figsize= (18,5))                        # ploting Barh graph.
plt.xticks(rotation=0,fontsize=16)
plt.title('Type Wise Rating Graph')
plt.barh(Type,Rating,edgecolor='black',linewidth=2,color=['#da93db', '#dbbe8a', '#ceeb81', '#9cd487', '#9aedb3','#9aedd7','#db8a8a'])
plt.xlabel('Rating')
plt.ylabel('Type Name')

Text(0, 0.5, 'Type Name')


Type3 = d1.groupby('cost_two_plates')['type'].unique().rename_axis('cost_two_plates').reset_index(name='Type Name')
Type3


d1['type'].unique()

array(['Buffet', 'Cafes', 'Delivery', 'Desserts', 'Dine-out',
       'Drinks & nightlife', 'Pubs and bars'], dtype=object)


Type_Name = ['Dine-out, Delivery, Desserts','Dine-out','Delivery','Delivery, Dine-out','Delivery, Drinks']

cost_two_plates = [80,70,60,50,40]


plt.figure(figsize= (10,5))                                  # ploting bar graph.
plt.xticks(rotation=90,fontsize=13)
plt.title('Cheapest Type in Bangalore')
plt.bar(Type_Name,cost_two_plates,color='c',edgecolor='black',linewidth=2)
plt.xlabel('Type_Name')
plt.ylabel('Cost_price')
plt.legend(['Minimum'])

<matplotlib.legend.Legend at 0x1fc76595400>


TYPE_NAME = ['Dine-out, Pubs and bars, Drinks & nightlife','Buffet, Dine-out','Dine-out, Drinks & nightlife','Dine-out','Pubs and bars']

COST_TWO_PLATES = [4000,4100,4500,5000,6000]


plt.figure(figsize= (10,5))                                  # ploting bar graph.
plt.xticks(rotation=90,fontsize=13)
plt.title('Location Wise Book Table Graph')
plt.bar(TYPE_NAME,COST_TWO_PLATES,color='lightgreen',edgecolor='black',linewidth=2)
plt.xlabel('Type Name')
plt.ylabel('Cost Price')
plt.legend(['Maximum'])

<matplotlib.legend.Legend at 0x1fc75f39790>


Type4 = d1.groupby('location')['type'].unique().rename_axis('Location').reset_index(name='Type Name')
Type4

	votes
restaurant
Onesta	347520
Truffles	301059
Empire Restaurant	229808
Hammered	180602
The Black Pearl	172122
Meghana Foods	129557
Barbeque Nation	108425
Smally's Resto Cafe	102877
Byg Brewski Brewing Company	99531
Gilly's Restobar	98808

	Name	Count	New_Name	geo_loc	lat	lon
0	BTM	5071	BangaloreBTM	(45.95485055, -112.49659530324134)	45.954851	-112.496595
1	HSR	2496	BangaloreHSR	(18.1475, 41.538889)	18.147500	41.538889
2	Koramangala 5th Block	2481	BangaloreKoramangala 5th Block	(12.9348429, 77.6189768)	12.934843	77.618977
3	JP Nagar	2219	BangaloreJP Nagar	(12.2655944, 76.6465404)	12.265594	76.646540
4	Whitefield	2109	BangaloreWhitefield	(53.5533682, -2.2969019)	53.553368	-2.296902

	Name	Count	New_Name	geo_loc	lan	lon
0	BTM	328	BangaloreBTM	(45.95485055, -112.49659530324134)	45.954851	-112.496595
1	Banashankari	36	BangaloreBanashankari	(15.8876779, 75.7046777)	15.887678	75.704678
2	Banaswadi	8	BangaloreBanaswadi	(13.0141618, 77.6518539)	13.014162	77.651854
3	Bannerghatta Road	75	BangaloreBannerghatta Road	(12.8545439, 77.5886892)	12.854544	77.588689
4	Basavanagudi	21	BangaloreBasavanagudi	(12.9417261, 77.5755021)	12.941726	77.575502

	Name	Count	New_Name	lan	lon
0	BTM	113	BangaloreBTM	45.954851	-112.496595
1	Banashankari	86	BangaloreBanashankari	15.887678	75.704678
2	Banaswadi	37	BangaloreBanaswadi	13.014162	77.651854
3	Bannerghatta Road	48	BangaloreBannerghatta Road	12.854544	77.588689
4	Basavanagudi	89	BangaloreBasavanagudi	12.941726	77.575502

	Location	Resturant
0	Peenya	1
1	Rajarajeshwari Nagar	2
2	Jakkur	3
3	Yelahanka	5
4	West Bangalore	6
5	Central Bangalore	8
6	Nagarbhavi	9
7	Kengeri	9
8	Hebbal	14
9	North Bangalore	14

	url	address	name	online_order	book_table	rate	votes	phone	location	rest_type	dish_liked	cuisines	approx_cost(for two people)	reviews_list	menu_item	listed_in(type)	listed_in(city)
0	https://www.zomato.com/bangalore/jalsa-banasha...	942, 21st Main Road, 2nd Stage, Banashankari, ...	Jalsa	Yes	Yes	4.1/5	775	080 42297555\r\n+91 9743772233	Banashankari	Casual Dining	Pasta, Lunch Buffet, Masala Papad, Paneer Laja...	North Indian, Mughlai, Chinese	800	[('Rated 4.0', 'RATED\n A beautiful place to ...	[]	Buffet	Banashankari
1	https://www.zomato.com/bangalore/spice-elephan...	2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ...	Spice Elephant	Yes	No	4.1/5	787	080 41714161	Banashankari	Casual Dining	Momos, Lunch Buffet, Chocolate Nirvana, Thai G...	Chinese, North Indian, Thai	800	[('Rated 4.0', 'RATED\n Had been here for din...	[]	Buffet	Banashankari
2	https://www.zomato.com/SanchurroBangalore?cont...	1112, Next to KIMS Medical College, 17th Cross...	San Churro Cafe	Yes	No	3.8/5	918	+91 9663487993	Banashankari	Cafe, Casual Dining	Churros, Cannelloni, Minestrone Soup, Hot Choc...	Cafe, Mexican, Italian	800	[('Rated 3.0', "RATED\n Ambience is not that ...	[]	Buffet	Banashankari
3	https://www.zomato.com/bangalore/addhuri-udupi...	1st Floor, Annakuteera, 3rd Stage, Banashankar...	Addhuri Udupi Bhojana	No	No	3.7/5	88	+91 9620009302	Banashankari	Quick Bites	Masala Dosa	South Indian, North Indian	300	[('Rated 4.0', "RATED\n Great food and proper...	[]	Buffet	Banashankari
4	https://www.zomato.com/bangalore/grand-village...	10, 3rd Floor, Lakshmi Associates, Gandhi Baza...	Grand Village	No	No	3.8/5	166	+91 8026612447\r\n+91 9901210005	Basavanagudi	Casual Dining	Panipuri, Gol Gappe	North Indian, Rajasthani	600	[('Rated 4.0', 'RATED\n Very good restaurant ...	[]	Buffet	Banashankari

	Restaurant
Location
Bellandur	346
Jayanagar	351
Bannerghatta Road	445
JP Nagar	498
Indiranagar	520
Marathahalli	651
HSR	678
Electronic City	691
BTM	695
Whitefield	812

	cost_two_plates	restaurant
26	200.0	Coffee Tindi
3	300.0	Addhuri Udupi Bhojana
24	300.0	Hide Out Cafe
18	300.0	T3H Cafe
20	400.0	The Vintage Cafe
19	400.0	360 Atoms Restaurant And Cafe
36	400.0	Fast And Fresh
13	450.0	Caf-Eleven
12	500.0	The Coffee Shack
34	500.0	Faasos
27	500.0	Sea Green Cafe
31	500.0	Foodiction
32	500.0	Sweet Truth
21	500.0	Woodee Pizza
9	550.0	Smacznego
28	550.0	Cuppa
29	550.0	Srinathji's Cafe

	restaurant.1
online_order	No	Yes
restaurant
11 to 11 Express Biriyanis	0	2
1441 Pizzeria	0	6
1722 Urban Bistro	0	11
1980s Games Cafe	0	5
1TO3 Kitchen	1	0
20 Feet High	0	19
22S	0	1
24 Hours Coffee Drop - La Classic	2	0
24@43 - The Oterra	2	0
2nd Home Restaurant	0	11
30 Square	0	11
37 China Town	2	0
3G's - Gulp & Groove	0	8
4 Mangoes	2	0

	restaurant.1
book_table	No	Yes
restaurant
#refuel	3	0
100ÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂ°C	3	0
1441 Pizzeria	6	0
18+ Ice Cafe	6	0
1Q1	0	16
20 Feet High	0	19
24 Carats - The Capitol	12	0
24/7 Food Service	4	0
2nd Home Restaurant	11	0
360 Atoms Restaurant And Cafe	7	0
3B's - Buddies, Bar & Barbecues	0	17
4 Mangoes	2	0

	rate	cost_two_plates	online_order
0	4.100000	800.0	Yes
1	4.100000	800.0	Yes
2	3.800000	800.0	Yes
3	3.700000	300.0	No
4	3.800000	600.0	No
...	...	...	...
51712	3.600000	1500.0	No
51713	3.700449	600.0	No
51714	3.700449	2000.0	No
51715	4.300000	2500.0	No
51716	3.400000	1500.0	No

	rate.1
book_table	No	Yes
rate
1.8	5	0
2.2	13	13
2.5	98	2
2.8	574	6
3.1	1540	10
3.4	2431	16
3.7	3590	218
3.9	3401	557
4.2	1204	951
4.5	233	423
4.8	23	43

	Location	Restaurant Name
0	BTM	[Sankranthi Veg Restaurant, Hearts Unlock Cafe...
1	Banashankari	[Jalsa, Spice Elephant, San Churro Cafe, Addhu...
2	Banaswadi	[Cafe Nibras, The Sanctuary, Crunch Pizzas, 9 ...
3	Bannerghatta Road	[Deja Vu Resto Bar, Fattoush, Empire Restauran...
4	Basavanagudi	[Grand Village, Timepass Dinner, Srinathji's C...
...	...	...
88	West Bangalore	[FreshMenu, Fit Dish Fetish, Garden City Mobil...
89	Whitefield	[Imperio Cafe, Night Diaries, LocalHost, AB's ...
90	Wilson Garden	[Tree Top, Sahana's (Nati Style), Karavali Kol...
91	Yelahanka	[Prashanth Naati Corner, Red Chillies Curries ...
92	Yeshwantpur	[Chef's Bank, New Agarwal Bhavan, Fishing Boat...

	restaurant
online_order	No	Yes
location
BTM	1766	3305
Banashankari	397	507
Banaswadi	302	343
Bannerghatta Road	687	924
Basavanagudi	243	441
Basaveshwara Nagar	87	100
Bellandur	519	752
Bommanahalli	121	115
Brigade Road	553	665
Brookefield	239	417

	restaurant
book_table	No	Yes
location
CV Raman Nagar	90	0
City Market	120	2
Domlur	427	55
Electronic City	1149	99
HSR	2279	217
Hosur Road	86	16
Infantry Road	117	34
Jalahalli	38	0
KR Puram	18	0
Kammanahalli	591	49
Koramangala	48	0
Koramangala 3rd Block	106	110
Koramangala 6th Block	1016	113
Kumaraswamy Layout	189	2
MG Road	547	347
Malleshwaram	635	89
Nagarbhavi	9	0
North Bangalore	14	0
Peenya	1	0
Rajajinagar	550	41

	rest_type	Counts
0	Quick Bites	37.237038
1	Casual Dining	20.084852
2	Cafe	7.208493
3	Delivery	5.040275
4	Dessert Parlor	4.389223
5	Takeaway, Delivery	3.937593
6	Bakery	2.230781
7	Casual Dining, Bar	2.221006
8	Beverage Shop	1.691171
9	Bar	1.341206
10	Food Court	1.210213
11	Sweet Shop	0.914992
12	Bar, Casual Dining	0.811371
13	Lounge	0.774224
14	Pub	0.697975

	rest_type	Counts
0	Quick Bites	19046
1	Casual Dining	10273
2	Cafe	3687
3	Delivery	2578
4	Dessert Parlor	2245
5	Takeaway, Delivery	2014
6	Bakery	1141
7	Casual Dining, Bar	1136
8	Beverage Shop	865
9	Bar	686
10	Food Court	619
11	Sweet Shop	468
12	Bar, Casual Dining	415
13	Lounge	396
14	Pub	357

	cuisines	Restaurant Name
0	African, Burger	[Galito's]
1	African, Burger, Desserts, Beverages, Fast Food	[Galito's]
2	American	[Quiznos, Uno Chicago Bar & Grill, Vimmi's Caf...
3	American, Asian, Continental, North Indian, So...	[Tapout Tavern]
4	American, Asian, European, North Indian	[Monkey Bar]
...	...	...
2699	Turkish, Fast Food, Biryani, Chinese	[Zu's Doner Kebaps]
2700	Turkish, Rolls	[Tarbouche]
2701	Vietnamese	[Vietnamese Kitchen, Hanoi - Vietnamese Cuisin...
2702	Vietnamese, Salad	[Phobidden Fruit]
2703	Vietnamese, Thai, Burmese, Japanese	[The Asian Curry House]

	votes
location
Koramangala 5th Block	2214827
Indiranagar	1172729
Koramangala 4th Block	685156
Church Street	594979
JP Nagar	586522
BTM	574058
Lavelle Road	505460
HSR	498772
Koramangala 7th Block	495289
Jayanagar	487044

	Rate	Rest_Type Name
0	1.8	[Casual Dining]
5	2.4	[Delivery, Casual Dining, Quick Bites, Bakery,...
10	2.9	[Delivery, Dessert Parlor, Casual Dining, Quic...
15	3.4	[Quick Bites, Bakery, Dessert Parlor, Casual D...
20	3.8	[Cafe, Casual Dining, Casual Dining, Cafe, Caf...
25	4.3	[Dessert Parlor, Quick Bites, Casual Dining, K...
30	4.8	[Quick Bites, Casual Dining, Bar, Casual Dinin...

	votes
cuisines
North Indian	516346
Pizza, Cafe, Italian	347520
Cafe, American, Burger, Steak	301059
North Indian, Chinese	258587
North Indian, Mughlai, South Indian, Chinese	227930
South Indian	161983
North Indian, Thai, Japanese, Continental, Cafe	161365
North Indian, European, Mediterranean	136485
North Indian, Continental, Chinese	122751
North Indian, European, Mediterranean, BBQ, Kebab	108425
North Indian, Mughlai	103738
Cafe, Italian, Burger, American, Steak	102877
Chinese	101736
Continental, North Indian, Italian, South Indian, Finger Food	99531
American, North Indian, Chinese, Finger Food, Momos	94271

	Rate	Cuisines Name
0	1.8	[North Indian, Continental, Chinese]
5	2.4	[North Indian, North Indian, Chinese, Thai, Pi...
10	2.9	[Chinese, Continental, Italian, North Indian, ...
15	3.4	[Biryani, North Indian, Chinese, Fast Food, Ba...
20	3.8	[Cafe, Mexican, Italian, North Indian, Rajasth...
25	4.3	[Ice Cream, Desserts, North Indian, Street Foo...
30	4.8	[South Indian, North Indian, European, Mediter...

	Type	Counts
0	Delivery	25653
1	Dine-out	17583
2	Desserts	3559
3	Cafes	1703
4	Drinks & nightlife	1091
5	Buffet	871
6	Pubs and bars	688

	Rate	Type Name
0	1.8	[Dine-out]
5	2.4	[Delivery, Dine-out, Desserts, Cafes, Drinks &...
10	2.9	[Delivery, Desserts, Dine-out, Cafes, Buffet, ...
15	3.4	[Delivery, Desserts, Dine-out, Cafes, Drinks &...
20	3.8	[Buffet, Cafes, Delivery, Desserts, Dine-out, ...
25	4.3	[Delivery, Desserts, Dine-out, Buffet, Cafes, ...
30	4.8	[Dine-out, Buffet, Pubs and bars, Delivery, Dr...

	cost_two_plates	Type Name
0	40.0	[Delivery, Dine-out]
1	50.0	[Delivery, Desserts, Cafes]
2	60.0	[Delivery]
3	70.0	[Dine-out]
4	80.0	[Dine-out, Delivery, Desserts]
...	...	...
65	4000.0	[Dine-out, Pubs and bars, Drinks & nightlife]
66	4100.0	[Buffet, Dine-out]
67	4500.0	[Dine-out, Drinks & nightlife]
68	5000.0	[Dine-out]
69	6000.0	[Dine-out]

Importing Library :-¶

Cleaning & Visualization the Data :-¶

Droping Some Columns Which is Not in Use¶

Droping Duplicates Values From Zomato Dataset¶

Cleaning Rate Columns¶

Filling Null Values in Rate Column¶

Droping Null Values from dataset¶

Renameing the Columns¶

Cleaning Cost_two_plates Column¶

1) Restaurant Wise Data Visualization¶

This Graph Shows Whitefield and BTM has the Maximum number of Restaurant¶

Cheapest Restaurant in Bangalore¶

Expensive Restaurant in Bangalore¶

Cost-effective Restaurant in Bangalore¶

This all Restaurant are affordable in Bngalore¶

Top 10 Best Budget Restaurant in Bangalore Location Wise¶

Highest Voted Restaurant in Bangalore¶

4 star Resturant under 500Rs¶

Most Popular Outlets in Bangalore¶

Geographical Visualization on Restaurant¶

it is clear that restuarant tend to concentrate in central bangalore area.¶

it can conclude that entrepreneurs can refer this and find out good locations for their Satrtup/venture.¶

Note: HeatMap help to visualize data in a better and understanding way which have lattitude,longitude or importance of particular place.¶

Heat Map of North Indian Restaurant.¶

Heat Map of South Indian Restaurant.¶

2) Data Visualization Based on Type of Order placed:¶

Online Order:¶

Max and Min Onlne Order Facility¶

Finding In Which Restaurant Online Order Facility is Avilable or Not¶

Finding How Many Rating a resturant is Getting When Online Order is avilable¶

3) Data Visualization on Table Booking:¶

Maximum and Minimmum Booked Table¶

Finding In Which Restaurant have option to Book Table¶

Finding How Much Rating get When Customer Book Table in Restaurant¶

4) Rate Wise Data Visualization¶

From This Scatterplot We can Observe that most of the highest rated accepts online order and they are in budget too¶

Conclusion : Almost more than 50 percent of restaurants has rating between 3 and 4 . In the rare Seniorio Where Reastaurants having 4.5 rating¶

Finding How Much Rating Get When Customer Booked Table in Restaurant¶

5) Location Wise Data Visualization¶

FInding Location Wise Restaurant¶

Most Famous Foodies Location in Bangalore¶

Location Wise Restaurant Name¶

Finding Which is the Best Location For Opening New Restaurant¶

This can be concluded from the above research that if someone wasnts to open a resturaunt, he may prefer to open the in the location where there is less no. of competitors to compete.¶

Finding In Which Location Online Order Facility is Available¶

As we now Bangalore is the high tech capital of india but there are some people who prefer to eat food at resturaunt arther than ordering online.¶

we can attract customers to book table while giving discounts on visit.¶

Location Wise Highest & Lowest Votes¶

6) Data Visualization Based on Restaurant Type:¶

Restaurant Type List¶

Top 15 Best Rest Type in Bangalore¶

As we all know, Banglore is Known as the tech Capital of India. so most of the modern people prefer Quick Bites,Casual Dining,Cafe and others Restaurants.¶

Location Wise Highest & Lowest Rate¶

7) Cuisines Wise Data Visualization¶

Most Popular Cuisines in Bangalore¶

We can observe that North Indian,South Indian and Biriyani are most common.¶

it means Bangalore is more influenced by North Indian Culture more than South.¶

Cuisines Wise Restaurant¶

In this given Restaurant You get all this Cuisines¶

Cuisines Wise Highest Votes¶

Which Cuisines Most Having Online Orders¶

In This Above graph it Conclusion That most of the people can order this given Cuisines in Bangalore.¶

Cuisines wise rating¶

8) Approx Cost Wise Data Visualization¶

Finding Best Restaurant¶

Most of the price between in a range of under 1000.¶

It means most are affortable & very few are luxurious.¶

Findiing Approx Cost for One People¶

Top 10 Most Expensive Restaurant with approx of cost of 2 people¶

Top 10 Most Cheapest Restaurant with approx of cost of 2 people¶

Cosst Two Plates Wise Data¶

We can conclusion that 300 and 400 cost dishes are maximum in count¶

9) Review List Wise Data Visualization¶

Cleaning Review Column¶

10) Dish Like Wise Data Visualization¶

11) Type Wise Data Visualization¶

Most Famous Type Where Coustomer Visit More¶

Finding Rate of Type¶

Cheapest V/S Expensive Type in Bangalore¶

Type Wise Location Data¶